Modelling elements of Smart Grids – Enhancing the OSeMOSYS (Open Source Energy Modelling System) code

Welsch, Manuel; Howells, Mark; Bazilian, Morgan; DeCarolis, Joseph F.; Hermann, Sebastian; Rogner, Holger

doi:10.1016/j.energy.2012.08.017

Cited by 109 publications

(64 citation statements)

References 39 publications

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“…The EnergyPLAN simulations are performed with technically optimized operation of closed energy system which calculates the flexibility gap as a value of CEEP. EnergyPLAN enables modeling of different operation principles and smart grid functions [50]. In addition, it models operation principles for combined heat and power, heat pumps and heat storage capacity [19].…”

Section: Energyplan Methodologymentioning

confidence: 99%

Two methods for decreasing the flexibility gap in national energy systems

Bjelić

Rajaković

Krajačić

et al. 2016

Energy

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Abstract:More variable renewable energy sources and energy efficiency measures create an additional flexibility gap and require a novel energy planning method for sustainable national energy systems. The firstly presented method uses only EnergyPLAN tool in order to decrease the flexibility gap in a national energy system. Generic Optimization program (GenOpt®) is an optimization program for the minimization of a cost function that is evaluated by an external simulation program, such as EnergyPLAN, which was used as the second method in this research. Successful strategies to decrease the flexibility gap are verified on the case of the Serbian national energy system using two methods for its structure design: (1) the iterative method, based on heuristics and manual procedure of using only EnergyPLAN, and (2) the optimization method, based on soft-linking of EnergyPLAN with GenOpt®. The latter method, named EPOPT (EnergyPlan-genOPT), found the solution for the structure of the sustainable national energy system at the total cost of 8,190 M€, while the iterative method was only able to find solutions at the cost in the range of 8,251-8,598 M€ by targeting only one sustainability goal. The advantages of the EPOPT method are its accuracy, user-friendliness and minimal costs, are valuable for planners. Highlights Heuristic and optimization method for sustainable national energy system structure  The same input assumptions resulting in different energy system structure  Both methods are successful in decreasing of the flexibility gap  The EPOPT method advantages are in the speed, accuracy and planner comfort  Advanced method for the sustainable national energy policy planning

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Section: Energyplan Methodologymentioning

confidence: 99%

Two methods for decreasing the flexibility gap in national energy systems

Bjelić

Rajaković

Krajačić

et al. 2016

Energy

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“…In contrast to the other ES evolution models, the time horizon consists of a series of single-year, instead of multi-year, periods. The programming code has been further elaborated and modified in order to incorporate key elements inherent to smart grids, such as prioritising of demands, demand shifting and storage [18]. Economic optimisation of the mix between these elements has been exemplified for a local urban energy system.…”

Section: Osemosysmentioning

confidence: 99%

“…Time slices aggregate time intervals over the year with similar conditions and thus have no inherent chronology, whereas time steps are sequential uniform paces in time. For modelling the behaviour of storage technologies, chronologic time steps are required, although time sequence can also be extracted from time slice definition [18]. ES evolution and ES optimisation models use time slice division, that may be hierarchically organised in seasonal, weekly and diurnal levels.…”

Section: Temporal Detailmentioning

confidence: 99%

Towards low carbon business park energy systems: Classification of techno-economic energy models

Timmerman

Vandevelde

Eetvelde

2014

Energy

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To mitigate climate destabilisation, human-induced greenhouse gas emissions urgently need to be curbed. A major share of these emissions originates from the industry and energy sectors. Hence, a low carbon shift in industrial and business park energy systems is called for. Low carbon business parks minimise energy-related carbon dioxide emissions by maximal exploitation of local renewable energy production, enhanced energy efficiency, and inter-firm heat exchange, combined in a collective energy system. The holistic approach of techno-economic energy models facilitates the design of such systems, while yielding an optimal trade-off between energetic, economic and environmental performances. However, no models custom-tailored for industrial park energy systems are detected in literature. In this paper, existing energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of models are selected and described. Subsequently, a practical typology is proposed, existing of energy system evolution, optimisation, simulation, accounting and integration models, and key model features are compared. Finally, important features for a business park energy model are identified. IntroductionFossil fuel based energy generation in the manufacturing industry and the sector's consumption of externally produced electricity and heat, are responsible for about 25% of total greenhouse gas emissions on European level [1]. Therefore, a low carbon shift in the energy system of industrial parks must be initiated. Low carbon business parks envision a collective energy system that employs energy efficient technologies, maximises the integration of local renewable energy sources and enables heat exchange between companies [2]. Technoeconomic energy models provide a holistic approach towards the configuration and operation of such systems, and facilitate the optimal trade-off between energetic, economic and environmental performances. To our knowledge, there is no energy model available that has been custom tailored for industrial parks and therefore, the development of such a model by adapting an existing model or by developing a new one is of high priority.Starting from the viewpoint of low carbon business park energy systems, this paper attempts to unravel the plethora of energy models, proposes a pragmatic model categorisation and identifies key model features. Section 2 describes the configuration of a business park energy system and its components and assesses the need for a holistic modelling approach. In section 3, several existing classifications of techno-economic energy models are screened for appropriate model features and based thereon a new classification is proposed. Its model categories are presented and exemplified throughout sections 4 to 9. Section 10 provides a clear comparison between these model types per key characteristic. In section 11, features for efficiently modelling business park energy systems are distilled. Finally, section 13 provides a summary.

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“…The smart grid is a concept with the purpose of intelligently integrating the generation, transmission and consumption of electricity through technological means [13,14]. Cyber-physical systems (CPS) are smart systems with physical and computational components, seamlessly integrated and interacting to sense the changing state of the world [15].…”

Section: Relationship To Cyber-physical Systemsmentioning

confidence: 99%